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Efficient Stochastic Simulation Algorithm for Chemically Reacting Systems Based on Support Vector Regression
Xin-jun Peng*,Yi-fei Wang
Author NameAffiliationE-mail
Xin-jun Peng* Department of Mathematics, Shanghai Normal University, Shanghai 200234, ChinaScientific Computing Key Laboratory of Shanghai Universities, Shanghai 200234, China xjpeng@shnu.edu.cn 
Yi-fei Wang Department of Mathematics, Shanghai University, Shanghai 200444, China  
Abstract:
The stochastic simulation algorithm (SSA) accurately depicts spatially homogeneous well-stirred chemically reacting systems with small populations of chemical species and properly represents noise, but it is often abandoned when modeling larger systems because of its computational complexity.In this work,a twin support vector regression based stochastic simulations algorithm (TS3A) is proposed by combining the twin support vector regression and SSA,the former is a well-known robust regression method in machine learning.Numeri-cal results indicate that this proposed algorithm can be applied to a wide range of chemically reacting systems and obtain significant improvements on effciency and accuracy with fewer simulating runs over the existing methods.
Key words:  Chemically reacting system, Stochastic simulation algorithm, Machine learn-ing, Support vector regression, Histogram distance
FundProject:
Efficient Stochastic Simulation Algorithm for Chemically Reacting Systems Based on Support Vector Regression
彭新俊*,王翼飞
摘要:
通过引入机器学习中的孪生支持向量回归(twin support vec-tor Regression,TSVR)算法,提出了一个将TSVR与随机模拟算法(stochastic simulation algorithm, SSA)相结合的基于孪生支持向量回归的随机模拟算法(twin support vector regression based stochastic simulations algorithm,TS3A).数值模拟实验表明该算法不仅能广泛应用于化学反应系统的模拟,并且在较少的模拟次数下可明显提高精度和效率.
关键词:  化学反应系统,随机模拟算法,机器学习,支持向量回归,直方图距离
DOI:10.1088/1674-0068/22/05/502-510
分类号: